plot,cloutput-method

plot,cloutput,missing-method

An object of class <code><a rd-options="" href="/link/cloutput?package=CMA&version=1.30.0" data-mini-rdoc="CMA::cloutput">cloutput</a></code> whose slot
           <code>probmatrix</code> does not contain any missing value,
           i.e. probability estimations are provided by the classifier.

main

A popular way of visualizing the output of classifier
             is to plot, separately for each class, the predicted
             probability of each predicted observations for the respective class.
             For this purpose, the plot area is divided into <code>K</code>
             parts, where <code>K</code> is the number of classes.
             Predicted observations are assigned, according to their
             true class, to one of those parts. Then, for each part
             and each predicted observation, the predicted probabilities
             are plotted, displayed by coloured dots, where each
             colour corresponds to one class.

multivariate

plot

This package provides a comprehensive collection of various microarray-based classification algorithms
both from Machine Learning and Statistics.
Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed
combined or stepwise in a user-friendly environment.

Christoph Bernau

Synthesis of microarray-based classification

plot function

An object of class <code><a href="cloutput" rd-options="">cloutput</a></code> whose slot
           <code>probmatrix</code> does not contain any missing value,
           i.e. probability estimations are provided by the classifier.

A popular way of visualizing the output of classifier
             is to plot, separately for each class, the predicted
             probability of each predicted observations for the respective class.
             For this purpose, the plot area is divided into <code>K</code>
             parts, where <code>K</code> is the number of classes.
             Predicted observations are assigned, according to their
             true class, to one of those parts. Then, for each part
             and each predicted observation, the predicted probabilities
             are plotted, displayed by coloured dots, where each
             colour corresponds to one class.

plot: Probability plot

Description

Arguments

Value

References

See Also